用粗粒度患者间差异训练模型，捕捉细微治疗响应变化。
LEARNER: Contrastive Pretraining for Learning Fine-Grained Patient Progression from Coarse Inter-Patient Labels
- 通过对比学习，利用患者间粗标签学习个体内部变化特征。
- 在肺超声和脑MRI数据上，分类准确率与F1分数均优于传统方法。
- 适合缺乏长期随访数据的个性化医疗研究者使用。
预测治疗是否带来有意义改善是精准医学的核心挑战，尤其当疾病进展表现为时间上的细微视觉变化时。尽管数据驱动的深度学习有望实现自动化预测，但为每位患者获取大规模纵向数据仍不现实。为此，我们探索是否可将患者间差异作为患者内部进展的代理信号。提出LEARNER框架，一种基于粗粒度患者间标签的对比预训练方法，用于学习细粒度、个体化的表征。在肺超声（LUS）和脑磁共振成像（MRI）数据集上，我们证明：在粗粒度患者间差异上训练的对比目标，能使模型捕捉到与治疗反应相关的细微患者内变化。在两种模态中，该方法均提升了下游分类准确率和F1分数，优于标准MSE预训练，表明患者间对比学习在个体化结局预测中的潜力。
原文摘要 · Abstract (English)
Predicting whether a treatment leads to meaningful improvement is a central challenge in personalized medicine, particularly when disease progression manifests as subtle visual changes over time. While data-driven deep learning (DL) offers a promising route to automate such predictions, acquiring large-scale longitudinal data for each individual patient remains impractical. To address this limitation, we explore whether inter-patient variability can serve as a proxy for learning intra-patient progression. We propose LEARNER, a contrastive pretraining framework that leverages coarsely labeled inter-patient data to learn fine-grained, patient-specific representations. Using lung ultrasound (LUS) and brain MRI datasets, we demonstrate that contrastive objectives trained on coarse inter-patient differences enable models to capture subtle intra-patient changes associated with treatment response. Across both modalities, our approach improves downstream classification accuracy and F1-score compared to standard MSE pretraining, highlighting the potential of inter-patient contrastive learning for individualized outcome prediction.
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